{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T02:11:54Z","timestamp":1773972714417,"version":"3.50.1"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100016252","name":"University of Macau Research Services and Knowledge Transfer Office","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100016252","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["SGDX20230821095159012"],"award-info":[{"award-number":["SGDX20230821095159012"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012245","name":"Science and Technology Planning Project of Guangdong Province","doi-asserted-by":"publisher","award":["2025A0505010016"],"award-info":[{"award-number":["2025A0505010016"]}],"id":[{"id":"10.13039\/501100012245","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004733","name":"Universidade de Macau","doi-asserted-by":"publisher","award":["SRG2023-00037-IOTSC"],"award-info":[{"award-number":["SRG2023-00037-IOTSC"]}],"id":[{"id":"10.13039\/501100004733","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004733","name":"Universidade de Macau","doi-asserted-by":"publisher","award":["MYRG-GRG2024-00284-IOTSC"],"award-info":[{"award-number":["MYRG-GRG2024-00284-IOTSC"]}],"id":[{"id":"10.13039\/501100004733","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52572354"],"award-info":[{"award-number":["52572354"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006469","name":"Fundo para o Desenvolvimento das Ci\u00eancias e da Tecnologia","doi-asserted-by":"publisher","award":["0007\/2025\/RIC"],"award-info":[{"award-number":["0007\/2025\/RIC"]}],"id":[{"id":"10.13039\/501100006469","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006469","name":"Fundo para o Desenvolvimento das Ci\u00eancias e da Tecnologia","doi-asserted-by":"publisher","award":["001\/2024\/SKL"],"award-info":[{"award-number":["001\/2024\/SKL"]}],"id":[{"id":"10.13039\/501100006469","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Accident Analysis & Prevention"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.aap.2026.108414","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T14:06:47Z","timestamp":1768572407000},"page":"108414","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["ROAR: Robust accident recognition and anticipation for autonomous driving"],"prefix":"10.1016","volume":"228","author":[{"given":"Xingcheng","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7839-1976","authenticated-orcid":false,"given":"Yanchen","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haicheng","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengbing","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0877-6829","authenticated-orcid":false,"given":"Zhenning","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.aap.2026.108414_b1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-024-48526-4","article-title":"A matched case-control analysis of autonomous vs human-driven vehicle accidents","volume":"15","author":"Abdel-Aty","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.aap.2026.108414_b2","article-title":"Analysis of safety benefits and security concerns from the use of autonomous vehicles: A grouped random parameters bivariate probit approach with heterogeneity in means","volume":"28","author":"Ahmed","year":"2020","journal-title":"Anal. Methods Accid. Res."},{"key":"10.1016\/j.aap.2026.108414_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107378","article-title":"Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review","volume":"194","author":"Ali","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108414_b4","doi-asserted-by":"crossref","unstructured":"Bao, W., Yu, Q., Kong, Y., 2020. Uncertainty-based traffic accident anticipation with spatio-temporal relational learning. In: Proceedings of the 28th ACM International Conference on Multimedia. MM\u201920.","DOI":"10.1145\/3394171.3413827"},{"key":"10.1016\/j.aap.2026.108414_b5","doi-asserted-by":"crossref","unstructured":"Bao, W., Yu, Q., Kong, Y., 2021. Drive: Deep reinforced accident anticipation with visual explanation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. ICCV, pp. 7619\u20137628.","DOI":"10.1109\/ICCV48922.2021.00752"},{"key":"10.1016\/j.aap.2026.108414_b6","series-title":"The Essential Guide To Image Processing","first-page":"143","article-title":"Chapter 7 - image noise models","author":"Boncelet","year":"2009"},{"key":"10.1016\/j.aap.2026.108414_b7","series-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"6154","article-title":"Cascade r-cnn: Delving into high quality object detection","author":"Cai","year":"2018"},{"key":"10.1016\/j.aap.2026.108414_b8","series-title":"Computer Vision \u2013 ACCV 2016","first-page":"136","article-title":"Anticipating accidents in dashcam videos","author":"Chan","year":"2017"},{"issue":"11","key":"10.1016\/j.aap.2026.108414_b9","doi-asserted-by":"crossref","first-page":"19954","DOI":"10.1109\/TITS.2022.3182410","article-title":"A review of vision-based traffic semantic understanding in itss","volume":"23","author":"Chen","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aap.2026.108414_b10","series-title":"2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9260","article-title":"Class-balanced loss based on effective number of samples","author":"Cui","year":"2019"},{"issue":"7","key":"10.1016\/j.aap.2026.108414_b11","doi-asserted-by":"crossref","first-page":"9601","DOI":"10.1109\/TITS.2022.3157254","article-title":"Traffic accident detection via self-supervised consistency learning in driving scenarios","volume":"23","author":"Fang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"4","key":"10.1016\/j.aap.2026.108414_b12","doi-asserted-by":"crossref","first-page":"1983","DOI":"10.1109\/TCSVT.2023.3307655","article-title":"Vision-based traffic accident detection and anticipation: A survey","volume":"34","author":"Fang","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.aap.2026.108414_b13","first-page":"6","article-title":"Dada: Driver attention prediction in driving accident scenarios","volume":"23","author":"Fang","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aap.2026.108414_b14","series-title":"2020 25th International Conference on Pattern Recognition","first-page":"2809","article-title":"Global feature aggregation for accident anticipation","author":"Fatima","year":"2021"},{"key":"10.1016\/j.aap.2026.108414_b15","series-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"14004","article-title":"Mist: Multiple instance self-training framework for video anomaly detection","author":"Feng","year":"2021"},{"key":"10.1016\/j.aap.2026.108414_b16","first-page":"104","article-title":"Back to the future: What do accident causation models tell us about accident prediction?","author":"Grant","year":"2018","journal-title":"Saf. Sci."},{"key":"10.1016\/j.aap.2026.108414_b17","series-title":"Proceedings of the 30th ACM International Conference on Multimedia","first-page":"2957","article-title":"Physical backdoor attacks to lane detection systems in autonomous driving","author":"Han","year":"2022"},{"issue":"7","key":"10.1016\/j.aap.2026.108414_b18","doi-asserted-by":"crossref","first-page":"9590","DOI":"10.1109\/TITS.2022.3155613","article-title":"A dynamic spatial\u2013temporal attention network for early anticipation of traffic accidents","volume":"23","author":"Karim","year":"2022","journal-title":"Trans. Intell. Transp. Sys."},{"issue":"1","key":"10.1016\/j.aap.2026.108414_b19","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1109\/TIV.2023.3275543","article-title":"An attention-guided multistream feature fusion network for early localization of risky traffic agents in driving videos","volume":"9","author":"Karim","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"issue":"1","key":"10.1016\/j.aap.2026.108414_b20","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1111\/mice.12989","article-title":"Context-aware trajectory prediction for autonomous driving in heterogeneous environments","volume":"39","author":"Li","year":"2024","journal-title":"Computer-Aided Civ. Infrastruct. Eng."},{"key":"10.1016\/j.aap.2026.108414_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107019","article-title":"Mitigating the impact of outliers in traffic crash analysis: A robust bayesian regression approach with application to tunnel crash data","volume":"185","author":"Li","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108414_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107446","article-title":"Efficient and robust estimation of single-vehicle crash severity: A mixed logit model with heterogeneity in means and variances","volume":"196","author":"Li","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108414_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2024.107760","article-title":"Real-time accident anticipation for autonomous driving through monocular depth-enhanced 3d modeling","volume":"207","author":"Liao","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108414_b24","series-title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","first-page":"5945","article-title":"Mftraj: Map-free, behavior-driven trajectory prediction for autonomous driving","author":"Liao","year":"2024"},{"key":"10.1016\/j.aap.2026.108414_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.commtr.2023.100116","article-title":"Gpt-4 enhanced multimodal grounding for autonomous driving: Leveraging cross-modal attention with large language models","volume":"4","author":"Liao","year":"2024","journal-title":"Commun. Transp. Res."},{"key":"10.1016\/j.aap.2026.108414_b26","series-title":"Proceedings of the 32nd ACM International Conference on Multimedia","first-page":"11041","article-title":"Crash: Crash recognition and anticipation system harnessing with context-aware and temporal focus attentions","author":"Liao","year":"2024"},{"key":"10.1016\/j.aap.2026.108414_b27","series-title":"2017 IEEE International Conference on Computer Vision","first-page":"2999","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017"},{"key":"10.1016\/j.aap.2026.108414_b28","series-title":"Proceedings of the 28th ACM International Conference on Multimedia","first-page":"4664","article-title":"Enhancing anomaly detection in surveillance videos with transfer learning from action recognition","author":"Liu","year":"2020"},{"key":"10.1016\/j.aap.2026.108414_b29","series-title":"Proceedings of the 30th ACM International Conference on Multimedia","first-page":"1279","article-title":"Rethinking open-world object detection in autonomous driving scenarios","author":"Ma","year":"2022"},{"issue":"7","key":"10.1016\/j.aap.2026.108414_b30","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A theory for multiresolution signal decomposition: the wavelet representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"10.1016\/j.aap.2026.108414_b31","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MSP.2016.2628914","article-title":"Automotive radars: A review of signal processing techniques","volume":"34","author":"Patole","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"10.1016\/j.aap.2026.108414_b32","series-title":"3rd International Conference on Learning Representations","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2015"},{"key":"10.1016\/j.aap.2026.108414_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.110071","article-title":"Dynamic attention augmented graph network for video accident anticipation","volume":"147","author":"Song","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.aap.2026.108414_b34","doi-asserted-by":"crossref","unstructured":"Suzuki, T., Kataoka, H., Aoki, Y., Satoh, Y., 2018. Anticipating traffic accidents with adaptive loss and large-scale incident db. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. CVPR.","DOI":"10.1109\/CVPR.2018.00371"},{"key":"10.1016\/j.aap.2026.108414_b35","series-title":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility","first-page":"11","article-title":"Predicting traffic accidents with event recorder data","author":"Takimoto","year":"2019"},{"issue":"C","key":"10.1016\/j.aap.2026.108414_b36","article-title":"Rareanom: A benchmark video dataset for rare type anomalies","volume":"140","author":"Thakare","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.aap.2026.108414_b37","doi-asserted-by":"crossref","unstructured":"Thakur, N., Gouripeddi, P., Li, B., 2024. Graph(graph): A nested graph-based framework for early accident anticipation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. WACV, pp. 7533\u20137541.","DOI":"10.1109\/WACV57701.2024.00736"},{"issue":"1","key":"10.1016\/j.aap.2026.108414_b38","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1109\/TIV.2023.3257169","article-title":"Gsc: A graph and spatio-temporal continuity based framework for accident anticipation","volume":"9","author":"Wang","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"10.1016\/j.aap.2026.108414_b39","first-page":"5599","article-title":"Deepaccident: A motion and accident prediction benchmark for v2x autonomous driving","volume":"vol. 38","author":"Wang","year":"2024"},{"key":"10.1016\/j.aap.2026.108414_b40","series-title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","first-page":"1172","article-title":"Weakly-supervised spatio-temporal anomaly detection in surveillance video","author":"Wu","year":"2021"},{"key":"10.1016\/j.aap.2026.108414_b41","series-title":"2020 5th International Conference on Universal Village","first-page":"1","article-title":"Real-time anomaly detection and feature analysis based on time series for surveillance video","author":"Xue","year":"2020"},{"key":"10.1016\/j.aap.2026.108414_b42","series-title":"2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"14662","article-title":"Generalized predictive model for autonomous driving","author":"Yang","year":"2024"},{"issue":"1","key":"10.1016\/j.aap.2026.108414_b43","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1109\/TPAMI.2022.3150763","article-title":"Dota: Unsupervised detection of traffic anomaly in driving videos","volume":"45","author":"Yao","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.aap.2026.108414_b44","series-title":"2019 International Conference on Robotics and Automation","first-page":"9711","article-title":"Egocentric vision-based future vehicle localization for intelligent driving assistance systems","author":"Yao","year":"2019"},{"key":"10.1016\/j.aap.2026.108414_b45","series-title":"Proceedings of the 27th ACM International Conference on Multimedia","first-page":"1805","article-title":"Anopcn: Video anomaly detection via deep predictive coding network","author":"Ye","year":"2019"},{"key":"10.1016\/j.aap.2026.108414_b46","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"13987","article-title":"Deep anomaly discovery from unlabeled videos via normality advantage and self-paced refinement","author":"Yu","year":"2022"},{"key":"10.1016\/j.aap.2026.108414_b47","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1330","article-title":"Agent-centric risk assessment: Accident anticipation and risky region localization","author":"Zeng","year":"2017"}],"container-title":["Accident Analysis & Prevention"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0001457526000230?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0001457526000230?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T01:20:22Z","timestamp":1773969622000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0001457526000230"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":47,"alternative-id":["S0001457526000230"],"URL":"https:\/\/doi.org\/10.1016\/j.aap.2026.108414","relation":{},"ISSN":["0001-4575"],"issn-type":[{"value":"0001-4575","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"ROAR: Robust accident recognition and anticipation for autonomous driving","name":"articletitle","label":"Article Title"},{"value":"Accident Analysis & Prevention","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aap.2026.108414","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"108414"}}